考虑罚金政策的众包物流服务质量网络均衡模型

为优化众包物流服务质量,考虑平台罚金政策,构建了包括发包方、众包平台和接包方在内的三层众包物流服务网络模型,并进行算例分析。结果表明,众包平台实施罚金政策并加大自身服务质量投入成本会促使接包方改善自身的服务质量,众包平台的服务质量和利润随之增大,但一味的增大罚金不但会使得接包方利润下降,众包平台的服务质量和利润也呈稍微下降趋势,因此平台应该选择合适的罚金区间;平台在竞争的同时也要进行一定的合作,因为平台间同步协调改进罚金政策以及质量投入会取得更大的收益和更高的平均服务质量。     

神经网络量子态及其应用

神经网络量子态是由人工神经网络所表示的量子态。得益于机器学习,尤其是深度学习近年来取得的突破性进展,神经网络量子态的研究得到了广泛的关注,成为当前的热点前沿方向。文章将介绍不同的神经网络量子态,其物理性质与典型应用场景,最新进展,以及面临的挑战。     

Erdös Rényi随机网络上爆炸渗流模型相变性质的数值模拟研究

基于改进的Newman和Ziff算法以及有限尺寸标度理论, 通过对表征渗流相变特征物理量的序参量、平均集团尺寸、二阶矩、标准偏差及尺寸不均匀性的数值模拟, 分析研究了Erdös Rényi随机网络上Achlioptas爆炸渗流模型的相变性质.研究表明: 尽管序参量表现出了不连续相变的特征, 但序参量以及其他特征物理量仍具有连续相变的幂律标度行为.因此严格地说, Erdös Rényi随机网络中的爆炸渗流相变是一种奇异相变, 它既不是标准的不连续相变, 又与常规随机渗流表现出的连续相变处于不同的普适类. 关键词： Erdös Rényi随机网络 爆炸渗流模型 相变 幂律标度行为     

基于动态混沌映射的三维加密正交频分复用无源光网络

提出了一种基于混沌映射的三维加密正交频分复用无源光网络保密通信系统.该系统通过相关性检测锁定收发端混沌系统参数,实现收发双方混沌系统同步;并利用同步混沌系统生成密钥,实现符号扰动以及二重子载波加密.该加密方案的密钥空间超过10~(86),能够有效对抗穷举攻击.实验实现了13.3 Gb/s基于64进制正交幅度调制的加密正交频分复用信号在25 km标准单模光纤中的传输,并完成了信息的有效解密.     

Anion-controlled geometrically different Cu(II) ion-based coordination polymers and green synthetic route for copper nanoparticles: a combined experimental and computational insight

Abstract

The Cu(II) ion-based polymeric complexes [Cu(2,2′-bpy).(N₃)₂]_n (I), [Cu₂(2,2′-bpy)₂.(N₃)₄]_n (II), and monomeric complex [Cu(2,2′-bpy).(NO₃)₂].5H₂O (III) have been synthesized with rigid (–N₃) and aromatic (2,2′-bpy = 2,2′-bipyridyl) ligand. The rigid azide group is responsible for the formation of 1-D extended structures in complexes I and II where as in the case of complex III, a monomeric complex is formed due to lack of a bridging group like –N₃, resulting in limitation in dimensionality. The thermal stability of the 1-D complexes is comparatively higher than monomeric complex III. Hirshfeld surface analysis has also been applied to investigate other weak interactions and compared with the results from single-crystal X-ray data. Due to the presence of paramagnetic metal centers and long metal···metal distances in complexes I and II and presence of lattice water molecules in complex III, decrease in luminescence intensities have been observed. To attain further insights into the aforementioned interesting species, some chemical concepts such as highest occupied molecular orbital–lowest unoccupied molecular orbital gap, electronic chemical potential, chemical hardness, and electrophilicity index, identified as a derivative of electronic energy, have also been emphasized employing the quantum chemical calculations in the framework of the density functional theory method using the M06-2X/ 6-31G** level of study. Further, these complexes have been used to synthesize copper nanoparticles by applying a green synthetic route.     

The (S−1,S) inventory model and its counterparts in queueing theory

We explore the relationship between the ($S?1,S$) inventory model and three well-known queueing models: the Erlang loss system, the machine-repair model and a two-node Jackson network. Exploiting this relationship allows us to obtain key performance measures of the ($S?1,S$) model, like the so-called virtual outdating time, the number of items on the shelf in steady state, the long-run rate of unsatisfied demands and the distribution of the empty shelf period.     

Artificial neural network‐based modeling of snow properties using field data and hyperspectral imagery

This study attempts to model snow wetness and snow density of Himalayan snow cover using a combination of Hyperspectral image processing and Artificial Neural Network (ANN). Initially, a total of 300 spectral signature measurements, synchronized with snow wetness and snow density, were collected in the field. The spectral reflectance of snow was then modeled as a function of snow properties using ANN. Four snow wetness and three snow density models were developed. A strong correlation was observed in near‐infrared and shortwave‐infrared region. The correlation analysis of ANN modeled snow density and snow wetness showed a strong linear relationship with field‐based data values ranging from 0.87–0.90 and 0.88–0.91, respectively. Our results indicate that an Artificial Intelligence (AI) approach, using a combination of Hyperspectral image processing and ANN, can be efficiently used to predict snow properties (wetness and density) in the Himalayan region. Recommendations for resource managers

• Snow properties, such as snow wetness and snow density are mainly investigated through field‐based survey but rugged terrains, difficult weather conditions, and logistics management issues establish remote sensing as an efficient alternative to monitor snow properties, especially in the mountain environment.
• Although Hyperspectral remote sensing is a powerful tool to conduct the quantitative analysis of the physical properties of snow, only a few studies have used hyperspectral data for the estimation of snow density and wetness in the Himalayan region. This could be because of the lack of synchronized snow properties data with field‐based spectral acquisitions.
• In combination with Hyperspectral image processing, Artificial Neural Network (ANN) can be a useful tool for effective snow modeling because of its ability to capture and represent complex input‐output relationships.
• Further research into understanding the applicability of neural networks to determine snow properties is required to obtain results from large snow cover areas of the Himalayan region.

     

Continuum versus discrete networks,graph Laplacians,and reproducing kernel Hilbert spaces

Motivated by applications to machine learning, we construct a reversible and irreducible Markov chain whose state space is a certain collection of measurable sets of a chosen l.c.h. space $\mathfrak{X}$. We study the resulting network (connected undirected graph), including transience, Royden and Riesz decompositions, and kernel factorization. We describe a construction for Hilbert spaces of signed measures which comes equipped with a new notion of reproducing kernels and there is a unique solution to a regularized optimization problem involving the approximation of $L^2$ functions by functions of finite energy. The latter has applications to machine learning (for Markov random fields, for example).     

On the Motion of Substance in a Channel of a Network: Extended Model and New Classes of Probability Distributions

We discuss the motion of substance in a channel containing nodes of a network. Each node of the channel can exchange substance with: (i) neighboring nodes of the channel, (ii) network nodes which do not belong to the channel, and (iii) environment of the network. The new point in this study is that we assume possibility for exchange of substance among flows of substance between nodes of the channel and: (i) nodes that belong to the network but do not belong to the channel and (ii) environment of the network. This leads to an extension of the model of motion of substance and the extended model contains previous models as particular cases. We use a discrete-time model of motion of substance and consider a stationary regime of motion of substance in a channel containing a finite number of nodes. As results of the study, we obtain a class of probability distributions connected to the amount of substance in nodes of the channel. We prove that the obtained class of distributions contains all truncated discrete probability distributions of discrete random variable ω which can take values 0,1,⋯,N. Theory for the case of a channel containing infinite number of nodes is presented in Appendix A. The continuous version of the discussed discrete probability distributions is described in Appendix B. The discussed extended model and obtained results can be used for the study of phenomena that can be modeled by flows in networks: motion of resources, traffic flows, motion of migrants, etc.